A rotation-invariant neural pattern recognition system, which can
recognize a rotated pattern and estimate its rotation angle, is considered.
It is well-known that humans sometimes recognize a rotated form by
means of mental rotation. The occurrence of mental rotation can be
explained in terms of the theory of information types. Therefore,
we first examine the applicability of the theory to a rotation-invariant
neural pattern recognition system. Next, we present a rotation-invariant
neural network which can estimate a rotation angle. The neural network
consists of a preprocessing network to detect the edge features of
input patterns and a trainable multilayered network. Furthermore,
a rotation-invariant neural pattern recognition system which includes
the rotation-invariant neural network is proposed. This system is
constructed on the basis of the above-mentioned theory. Finally,
it is shown that, by means of computer simulations of a binary pattern
and a coin recognition problem, the system is able to recognize rotated
patterns and estimate their rotation angle
%0 Journal Article
%1 Fukumi1997
%A Fukumi, Minoru
%A Omatu, Sigeru
%A Nishikawa, Yoshikazu
%D 1997
%J IEEE Transactions on Neural Networks
%K angle backpropagation, coin detection, edge estimation, feedforward mental multilayered nets, network, neural orientation pattern problem, recognition recognition, rotated rotation rotation, rotation-invariance rotationbackpropagation, selectivity,
%N 3
%P 568-581
%R 10.1109/72.572096
%T Rotation-invariant neural pattern recognition system estimating a
rotation angle
%U http://ieeexplore.ieee.org/iel4/72/12383/00572096.pdf?tp=&arnumber=572096&isnumber=12383
%V 8
%X A rotation-invariant neural pattern recognition system, which can
recognize a rotated pattern and estimate its rotation angle, is considered.
It is well-known that humans sometimes recognize a rotated form by
means of mental rotation. The occurrence of mental rotation can be
explained in terms of the theory of information types. Therefore,
we first examine the applicability of the theory to a rotation-invariant
neural pattern recognition system. Next, we present a rotation-invariant
neural network which can estimate a rotation angle. The neural network
consists of a preprocessing network to detect the edge features of
input patterns and a trainable multilayered network. Furthermore,
a rotation-invariant neural pattern recognition system which includes
the rotation-invariant neural network is proposed. This system is
constructed on the basis of the above-mentioned theory. Finally,
it is shown that, by means of computer simulations of a binary pattern
and a coin recognition problem, the system is able to recognize rotated
patterns and estimate their rotation angle
@article{Fukumi1997,
abstract = {A rotation-invariant neural pattern recognition system, which can
recognize a rotated pattern and estimate its rotation angle, is considered.
It is well-known that humans sometimes recognize a rotated form by
means of mental rotation. The occurrence of mental rotation can be
explained in terms of the theory of information types. Therefore,
we first examine the applicability of the theory to a rotation-invariant
neural pattern recognition system. Next, we present a rotation-invariant
neural network which can estimate a rotation angle. The neural network
consists of a preprocessing network to detect the edge features of
input patterns and a trainable multilayered network. Furthermore,
a rotation-invariant neural pattern recognition system which includes
the rotation-invariant neural network is proposed. This system is
constructed on the basis of the above-mentioned theory. Finally,
it is shown that, by means of computer simulations of a binary pattern
and a coin recognition problem, the system is able to recognize rotated
patterns and estimate their rotation angle},
added-at = {2011-03-27T19:47:06.000+0200},
affiliation = {University of Tokushima, Faculty of Engineering, Department of Information
Science and Intelligent Systems},
author = {Fukumi, Minoru and Omatu, Sigeru and Nishikawa, Yoshikazu},
biburl = {https://www.bibsonomy.org/bibtex/29b3cb68c08f5f72272d5071966e2bb3e/cocus},
doi = {10.1109/72.572096},
file = {:./fukumi1997_00572096.pdf:PDF},
interhash = {f0d8160854d474e3d3870eca535d0f69},
intrahash = {9b3cb68c08f5f72272d5071966e2bb3e},
issn = {1045-9227},
journal = {{IEEE} Transactions on Neural Networks},
keywords = {angle backpropagation, coin detection, edge estimation, feedforward mental multilayered nets, network, neural orientation pattern problem, recognition recognition, rotated rotation rotation, rotation-invariance rotationbackpropagation, selectivity,},
month = may,
number = 3,
owner = {CK},
pages = {568-581},
timestamp = {2011-03-27T19:47:08.000+0200},
title = {Rotation-invariant neural pattern recognition system estimating a
rotation angle},
url = {http://ieeexplore.ieee.org/iel4/72/12383/00572096.pdf?tp=&arnumber=572096&isnumber=12383},
urldate = {25-1-2008},
volume = 8,
year = 1997
}